9 citations · 25 across the 21 of their papers we have counts for
6 papers · 1 filter
U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching
Junsheng Zhou, Xingyu Shi, Haichuan Song +3
Point clouds captured by scanning sensors are often perturbed by noise, which have a highly negative impact on downstream tasks (e.g. surface reconstruction and shape understanding…
Kling-Avatar: Grounding Multimodal Instructions for Cascaded Long-Duration Avatar Animation Synthesis
Yikang Ding, Jiwen Liu, Wenyuan Zhang +11
Recent advances in audio-driven avatar video generation have significantly enhanced audio-visual realism. However, existing methods treat instruction conditioning merely as low-lev…
GAP: Gaussianize Any Point Clouds with Text Guidance
Weiqi Zhang, Junsheng Zhou, Haotian Geng +2
3D Gaussian Splatting (3DGS) has demonstrated its advantages in achieving fast and high-quality rendering. As point clouds serve as a widely-used and easily accessible form of 3D r…
MonoInstance: Enhancing Monocular Priors via Multi-view Instance Alignment for Neural Rendering and Reconstruction
Wenyuan Zhang, Yixiao Yang, Han Huang +4
Monocular depth priors have been widely adopted by neural rendering in multi-view based tasks such as 3D reconstruction and novel view synthesis. However, due to the inconsistent p…
NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstruction
Wenyuan Zhang, Emily Yue-ting Jia, Junsheng Zhou +4
Recently, it has shown that priors are vital for neural implicit functions to reconstruct high-quality surfaces from multi-view RGB images. However, current priors require large-sc…
Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation
Takeshi Noda, Chao Chen, Junsheng Zhou +3
Inferring signed distance functions (SDFs) from sparse point clouds remains a challenge in surface reconstruction. The key lies in the lack of detailed geometric information in spa…